Project code for Udacity's AI Programming with Python Nanodegree program. In this project, students first develop code for an image classifier built with PyTorch, then convert it into a command line application.
You'll need the popular machine leanring tools installed in your environment to run this project. Run the following commands below
pip install numpy pandas matplotlib pil
or if using conda
conda install numpy pandas matplotlib pil
Run the file train.py to train the model to use
Basic command : python train.py data_directory It should print current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Optional Arguments : To save a checkpoint set a save directory as such : python train.py data_dir --save_dir save_checkpoint_directory To select an arcitecture : python train.py data_dir --arch "vgg16" Setting hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --device gpu Output: A trained network ready and saved at a checkpoint for prediccting flower images and identifying the species
Use predict.py to predict flower types with probability of that flower. Pass in the path to the image you wish to predict as an absolute path /path/to/image.jpg and return the flower name and class probability
Basic usage: python predict.py /path/to/image.jpg checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu